Semi-structured Information Retrieval in Clinical Text for Cohort Identification

描述（由申请人提供）：自然语言处理（NLP）技术已显示出从电子健康记录（EHR）的自由文本中提取数据的前景，但研究一致发现，该技术并不容易在应用程序设置中推广。不幸的是，将NLP应用于真实的用例的大部分焦点仍然停留在单一的、定义良好的应用程序设置的范例上，因此对看不见的用例的可推广性仍然隐含地没有得到解决。我们建议通过采用信息检索（IR）的角度，明确考虑到看不见的应用程序设置，目标是患者级别的队列识别。为此，我们引入了分层语言模型，一个IR框架，使NLP产生的工件的重用。我们的长期目标是通过提供强大的、以用户为中心的工具来加速对患者健康和疾病的调查，这些工具是处理、检索和利用EHR的自由文本所必需的。这项建议的主要目标是准确地检索特设的，现实的队列从临床文本在马约诊所和OHSU，建立方法，资源，并评估患者水平的IR。我们假设，队列识别可以解决一个普遍的方式由一个新的IR框架：分层的语言模型。我们将通过四个具体目标来检验这一假设。在目标1中，我们将在分层语言IR框架中搜索医学NLP工件。这涉及到存储和索引NLP工件，以及使用统计语言模型来检索基于文本及其相关NLP工件的文档。在目标2中，我们处理的实际设置的特设队列识别，移动到患者级（而不是文件级）IR。为了准确地处理患者队列中，合格的证据可能会分布在多个文件，我们将开发和实施患者级检索模型，占跨文档的关系和时间组合的事件。在目标3中，我们将使用来自两个站点的EHR数据构建并行IR测试集合;由多个站点编写的不同队列查询集。 人们对各种临床或流行病学的目的;以及评估哪些患者与两个站点的哪些查询相关。最后，在目标4中，我们在特定队列识别任务上改进和评估患者级分层语言IR，在用户、查询、优化指标和机构之间进行比较。我们将与现有技术进行额外的外部比较，例如，电子病历和基因组网络的队列。拟议工作的预期成果是：（i）一个开源的队列识别工具，可供临床医生和流行病学家使用，原则上使用NLP工件进行看不见的查询;（ii）一个并行的队列识别测试集，包括两个机构内的文件集，不同的测试主题和用户生成的文本查询，以及与每个查询相关的患者水平判断;以及（iii）通过检索患者队列的任务验证医学NLP的可重用性。

DESCRIPTION (provided by applicant): Natural Language Processing (NLP) techniques have shown promise for extracting data from the free text of electronic health records (EHRs), but studies have consistently found that techniques do not readily generalize across application settings. Unfortunately, most of the focus in applying NLP to real use cases has remained on a paradigm of single, well-defined application settings, so that generalizability to unseen use cases remains implicitly unaddressed. We propose to explicitly account for unseen application settings by adopting an information retrieval (IR) perspective with the objective of patient-level cohort identification. To do so, we introduce layered language models, an IR framework that enables the reuse of NLP-produced artifacts. Our long term goal is to accelerate investigations of patient health and disease by providing robust, user- centric tools that are necessary to process, retrieve, and utilize the free text of EHRs. The main goal of this proposal is to accurately retrieve ad hoc, realistic cohorts from clinical text at Mayo Clinic and OHSU, establishing methods, resources, and evaluation for patient-level IR. We hypothesize that cohort identification can be addressed in a generalizable fashion by a new IR framework: layered language models. We will test this hypothesis through four specific aims. In Aim 1, we will make medical NLP artifacts searchable in our layered language IR framework. This involves storing and indexing the NLP artifacts, as well as using statistical language models to retrieve documents based on text and its associated NLP artifacts. In Aim 2, we deal with the practical setting of ad hoc cohort identification, moving to patient-level (rather than document-level) IR. To accurately handle patient cohorts in which qualifying evidence may be spread over multiple documents, we will develop and implement patient-level retrieval models that account for cross- document relational and temporal combinations of events. In Aim 3, we will construct parallel IR test collections using EHR data from two sites; a diverse set of cohort queries written by multiple people toward various clinical or epidemiological ends; and assessments of which patients are relevant to which queries at both sites. Finally, in Aim 4, we refine and evaluate patient-level layered language IR on the ad hoc cohort identification task, making comparisons across the users, queries, optimization metrics, and institutions. We will draw additional extrinsic comparisons with pre-existing techniques, e.g., for cohorts from the Electronic Medical Records and Genonmics network. The expected outcomes of the proposed work are: (i) An open-source cohort identification tool, usable by clinicians and epidemiologists, that makes principled use of NLP artifacts for unseen queries; ii) A parallel test collection for cohort identification, includig two intra-institutional document collections, diverse test topics and user-produced text queries, and patient-level judgments of relevance to each query; and (iii) Validation of the reusability of medical NLP via the task of retrieving patient cohorts.